ISAT.segment_any package

Submodules

ISAT.segment_any.gpu_resource module

class ISAT.segment_any.gpu_resource.GPUResource_Thread(gpu_id=0)

Bases: QThread

The thread for monitoring GPU0 resources.

Parameters:

gpu_id (int) – The id of the GPU device to monitor. default 0.

message

int = …, arguments: Sequence = …) -> PYQT_SIGNAL

types is normally a sequence of individual types. Each type is either a type object or a string that is the name of a C++ type. Alternatively each type could itself be a sequence of types each describing a different overloaded signal. name is the optional C++ name of the signal. If it is not specified then the name of the class attribute that is bound to the signal is used. revision is the optional revision of the signal that is exported to QML. If it is not specified then 0 is used. arguments is the optional sequence of the names of the signal’s arguments.

Type:

pyqtSignal(*types, name

Type:

str = …, revision

run(self)

ISAT.segment_any.model_zoo module

ISAT.segment_any.segment_any module

class ISAT.segment_any.segment_any.SegAny(checkpoint, use_bfloat16=True)

Bases: object

Segment Anything model class.

Parameters:

  • checkpoint (str) – checkpoint path.

  • use_bfloat16 (bool) – model dtype is bfloat16. default is True.

checkpoint

checkpoint path.

Type:

str

model_dtype

model dtype. eg: torch.bfloat16, torch.float32

Type:

torch.dtype

model_source

model source. eg: mobile_sam, sam_hq, sam2, sam2.1, …

Type:

str

model_type

model type. eg: vit_b, vit_t, hiera_small, …

Type:

str

predictor

Sam Predictor.

Type:

SamPredictor

set_image(image)

Set image to segment.

Parameters:

image (np.ndarray) – The image to segment.

Return type:

None

reset_image()

Reset image to segment.

Return type:

None

predict_with_point_prompt(input_point, input_label)

Segment with point prompt.

Parameters:

  • input_point (list | np.ndarray) – The input points. [(x1, y1), (x2, y2), …]

  • input_label (list | np.ndarray) – The label of points, support value 0 or 1. [0, 0, …]

Return type:

Tensor

predict_with_box_prompt(box)

Segment with box prompt.

Parameters:

box (list | np.ndarray) – [xmin, ymin, xmax, ymax]

Return type:

Tensor

class ISAT.segment_any.segment_any.SegAnyVideo(checkpoint, use_bfloat16=True)

Bases: object

Segment Anything model class for video segmentation. Only SAM2 or SAM2.1 model.

Parameters:

  • checkpoint (str) – checkpoint path.

  • use_bfloat16 (bool) – model dtype is bfloat16. default is True.

checkpoint

checkpoint path.

Type:

str

model_dtype

model dtype. eg: torch.bfloat16, torch.float32

Type:

torch.dtype

model_source

model source. sam2 or sam2.1 .

Type:

str

model_type

model type. eg: hiera_tiny, hiera_small, hiera_base_plus or hiera_large.

Type:

str

predictor

Sam Predictor.

Type:

SamPredictor

init_state(image_root, image_name_list, offload_video_to_cpu=True, offload_state_to_cpu=True, async_loading_frames=False)

Init state for video segmentation. Need read all images.

Parameters:

  • image_root (str) – The directory of images.

  • image_name_list (list) – list of image file names to segment.

  • offload_video_to_cpu (bool) – offload frames to CPU.

  • offload_state_to_cpu (bool) – offload state to CPU.

  • async_loading_frames (bool) – asynchronize loading frames.

Return type:

None

reset_state()

Remove all input points or mask in all frames throughout the video.

Return type:

None

add_new_mask(frame_idx, ann_obj_id, mask)

Add new mask prompt for video segmentation.

Parameters:

  • frame_idx (int) – Frame index.

  • ann_obj_id (int) – Object ID.

  • mask (torch.Tensor | np.ndarray) – New mask prompt.

Return type:

None

Module contents